Coupling STAR-CCM+ with Optimization Software IOSO by · PDF fileThe main task of this work...
Transcript of Coupling STAR-CCM+ with Optimization Software IOSO by · PDF fileThe main task of this work...
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Coupling STAR-CCM+ with Optimization Software
IOSO by the example of axial 8-stages jet engine
compressor.
Folomeev V., (Sarov Engineering Center)
Iakunin A., (JSC Klimov)
STAR Global Conference 2014, March 17-19, Vienna
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Objectives
STAR Global Conference 2014, March 17-19, Vienna
To create the procedure for coupling CFD code STAR-
CCM+ with optimization software IOSO
To improve the law of turning guide vanes for the first
tree stages of axial compressor.
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Main steps
STAR Global Conference 2014, March 17-19, Vienna
Create axial 8-stages compressor CFD model
Develop the optimization task
IOSO supports only windows platform when clusters
usually operate on Linux, thus we need do develop the
procedure for net connection
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Typical design iterations
STAR Global Conference 2014, March 17-19, Vienna
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CFD model
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Steady model with
mixing plane interfaces.
Low-Reynolds k-omega
turbulence model
Turbulence
Suppression model
Polyhedral mesh about
15 mln cells with near
wall prism layers
Optimization process
involved 500 cores of
IBM cluster
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CFD modeling visualization
STAR Global Conference 2014, March 17-19, Vienna
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CFD modeling visualization
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CFD modeling visualization
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First rotating row
Pressure side Suction side
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CFD modeling convergence history
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Results of CFD calculation
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Main features of the IOSO optimization technology
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IOSO Technology is based on the response surface technology. That is why the strategy differs
significantly from the well-known approaches to optimization. The strategy has higher efficiency and
provides wider range of capabilities than standard algorithms. The main advantage one can get from using
the IOSO Technology is ability to solve very complex optimization tasks.
IOSO Technology algorithms:
Are independent of the optimization task types
Have good global properties and in the majority of the cases are able to find the global optimum
Have high convergence rate and allow to quickly and efficiently find the region where optimum is located
Are highly robust with the respect to the computational process
Allow for robust solving of stochastic optimization tasks, even if such problems have high level of noise
Provide the capabilities to solve real-life optimization tasks that involve complex modern high fidelity
mathematical models or engineering applications (for example, 3D CAD, CFD or FEA software).
Allow for solving robust design optimization tasks, including multidimensional single and multiobjective
optimization tasks.
Are very simple to use even for solving complex practical tasks of nonlinear optimization.
Have full-automatic optimization algorithms which do not need to be tuned up by a user.
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Optimization task
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Independent
parameters
Leading vane angle
1-stage stator angle
2-stage stator angle
constrained
parameters
Mass flow
Pressure rise
Objective
function
efficiency
For each rotational rate we solve independent optimization task
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Setting the optimization task
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IOSO uses file base coupling with STAR-CCM+
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Scripting STAR-CCM+ macro
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STAR-CCM+ macro provides full java
functionality including net sockets and
supports ssh libraries. This
was used in creation client-server
application.
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Scheme of coupling
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IOSO generates text file with input parameters
IOSO runs Server.java
Server.java reads
input file
Server.java runs ssh on
remote cluster and
starts STAR-CCM+
with macro
Server.java opens
server sockets and
sends input
variables
STAR-CCM+ with macro receives
input variables, performs CFD
calculations and sends back to
Server.java output variables
Server.java receives
output values and
writes output file
for IOSO
IOSO reads output file and analyses parameters
for a next iteration
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Results of optimization
STAR Global Conference 2014, March 17-19, Vienna
The normal rotation rate regime was examined firstly
It was found that optimal configuration is nearly to
zero for all variable angles with variation less than 0.5
degree. This is consistent with analytical compressor
design methods.
The convergence of the optimization process was
reached within 30 iterations. It approximately corresponds
to 1 week of calculation on 500 cores of modern cluster
solution.
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Conclusions
STAR Global Conference 2014, March 17-19, Vienna
The main task of this work was to develop procedure
for coupling CFD code STAR-CCM+ with optimization
software IOSO. This task was reached.
Axial 8-stages compressor served as an example for
the main task. The robust CFD model of the compressor
was created and tested. This model is appropriate for the
automatic calculations with varying geometry parameters.
The results of the optimization process are in
agreement with other analytical methods of prediction
compressor performance. It is presumed that the accuracy
may be improved with complication of CFD model, for
instance including harmonic balance model.
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Thank you for your attention
STAR Global Conference 2014, March 17-19, Vienna